How Can organizations achieve their diversity goals? New theories and evidence
Bibliographic record
Abstract
Many organizations set-up well-intentioned diversity goals but find that the reality falls short of their expectations. We take up this problem – how organizations can achieve their diversity goals – by illuminating how the behaviors of individual actors undermine ambitious goals and well-intentioned practices. In this symposium, we bring together four papers that explore novel mechanisms that elucidate how micro-level actions shape the efficacy of meso-level diversity initiatives and macro-outcomes of interest to DEI scholars. The papers’ approaches draw on several perspectives, including those rooted in psychodynamics, culture studies and social networks, and diverse methodologies, including longitudinal data, ethnography, experiments, and interviews. Jennifer Merluzzi is especially fitting as the discussant, as diversity in organizations is one of the major themes in her work. A Systems Psychodynamic Perspective on Racial Inequality in Organizations Author: Sanaz Mobasseri; Boston U. Questrom School of Business Act the Part: Race and Social Class in the Modern Workplace Author: Summer Jackson; Harvard Business School Constrained by Good Intentions: Unintended Consequences of Fair-Minded Hiring Rules Author: Jeraul Mackey; Rady School of Management, U. of California San Diego Do referrals disadvantage non-White applicants? Evidence from Silicon Valley tech firms Author: Elena Obukhova; McGill U.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".